United States

Twitter is one of the most popular social networks attracting millions of users, while a considerable proportion of online discourse is captured. It provides a simple usage framework with short messages and an efficient application programming interface (API) enabling the research community to study and analyze several aspects of this social network. However, the Twitter usage simplicity can lead to malicious handling by various bots. The malicious handling phenomenon expands in online discourse, especially during the electoral periods, where except the legitimate bots used for dissemination and communication purposes, the goal is to manipulate the public opinion and the electorate towards a certain direction, specific ideology, or political party.

This paper focuses on the design of a novel system for identifying Twitter bots based on labeled Twitter data. To this end, a supervised machine learning (ML) framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm, where the hyper-parameters are tuned via cross-validation. Our study also deploys Shapley Additive Explanations (SHAP) for explaining the ML model predictions by calculating feature importance, using the game theoretic-based Shapley values. Experimental evaluation on distinct Twitter datasets demonstrate the superiority of our approach, in terms of bot detection accuracy, when compared against a recent state-of-the-art Twitter bot detection method.

ICWSM 2022

Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case Study

us 2020 presidential elections

explainable machine learning model

twitter bot detection

**ICWSM-2022 will be held from June 6th – 9th in a hybrid format, both in-person in Atlanta, Georgia and online.**

**The Workshop and Tutorial Sessions are accessible to those that registered separately for these sessions. If you registered to attend but are not able to access the these sessions from the [Schedule tab](https://underline.io/events/291/schedule?day=2022-06-06T04%3A00%3A00.000Z), please email icwsm@underline.io
**

The International AAAI Conference on Web and Social Media (ICWSM) is a forum for researchers from multiple disciplines to come together to share knowledge, discuss ideas, exchange information, and learn about cutting-edge research in diverse fields with the common theme of online social media. This overall theme includes research in new perspectives in social theories, as well as computational algorithms for analyzing social media. ICWSM is a singularly fitting venue for research that blends social science and computational approaches to answer important and challenging questions about human social behavior through social media while advancing computational tools for vast and unstructured data.


ICWSM, now in its sixteenth year, has become one of the premier venues for computational social science, and previous years of ICWSM have featured papers, posters, and demos that draw upon network science, machine learning, computational linguistics, sociology, communication, and political science. The uniqueness of the venue and the quality of submissions have contributed to a rapidly growing conference, and a competitive acceptance rate of approximately 20% for full-length research papers published in the proceedings by the Association for the Advancement of Artificial Intelligence (AAAI).


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The International AAAI Conference on Web and Social Media (ICWSM) is a forum for researchers from multiple disciplines to come together to share knowledge, discuss ideas, exchange information, and learn about cutting-edge research in diverse fields with the common theme of online social media. This overall theme includes research in new perspectives in social theories, as well as computational algorithms for analyzing social media. ICWSM is a singularly fitting venue for research that blends social science and computational approaches to answer important and challenging questions about human social behavior through social media while advancing computational tools for vast and unstructured data.

technical paper

Most platforms, including Reddit, face a dilemma when applying interventions such as subreddit bans to toxic communities — do they risk angering their user base by proactively enforcing stricter controls on discourse or do they defer interventions at the risk of eventually triggering negative media reactions which might impact their advertising revenue? In this paper, we analyze Reddit’s previous administrative interventions to understand one aspect of this dilemma: the relationship between the media and administrative interventions. More specifically, we make two primary contributions. First, using a mediation analysis framework, we find evidence that Reddit’s interventions for violating their content policy for toxic content occur because of media pressure. Second, using interrupted time series analysis, we show that media attention on communities with toxic content only increases the problematic behavior associated with that community (both within the community itself and across the platform). However, we find no significant difference in the impact of administrative interventions on subreddits with and without media pressure. Taken all together, this study provides evidence of a media-driven moderation strategy at Reddit and also suggests that such a strategy may not have a significantly different impact than a more proactive strategy.

Exploring the magnitude and effects of media influence on Reddit moderation

Online conversations, just like offline ones, are susceptible to influence by bad actors. These users have the capacity to derail neutral or even prosocial discussions through adverse behavior. Moderators and users alike would benefit from more resilient online conversations, i.e., those that can survive the influx of adverse behavior to which many conversations fall victim. In this paper, we examine the notion of conversational resilience: what makes a conversation more or less capable of withstanding an adverse interruption? Working with 11.5M comments from eight mainstream subreddits, we compiled more than 5.8M comment threads (i.e., conversations). Using 239K relevant conversations, we examine how well comment, user and subreddit characteristics can predict conversational outcomes. More than half of all conversations proceed after the first adverse event. Six out of ten conversations that proceed result in future removals. Comments violating platform-wide norms and those written by authors with a history of norm violations lead to not only more norm violations, but also fewer prosocial outcomes. However, conversations in more populated subreddits and conversations where the first adverse event's author was initially a strong contributor are capable of minimizing future removals and promoting prosocial outcomes after an adverse event. By understanding factors that contribute to conversational resilience we shed light onto what types of behavior can be encouraged to promote prosocial outcomes even in the face of adversity.

Conversational Resilience: Quantifying and Predicting Conversational Outcomes Following Adverse Events

Reddit has found its communities playing a prominent role in originating and propagating problematic sociopolitical dis- course. Reddit administrators have generally struggled to prevent or contain such discourse for several reasons including: (1) the inability for a handful of human administrators to track and react to millions of posts and comments per day and (2) fear of backlash as a consequence of administrative decisions to ban or quarantine hateful communities.  Consequently, administrative actions (community bans and quarantines) are often taken only when problematic discourse within a community spills over into the real world with serious consequences. In this paper, we investigate the feasibility of deploying tools to proactively identify problematic communities on Reddit. Proactive identification strategies show promise for three reasons: (1) they have potential to reduce the manual efforts required to track communities for problematic content, (2) they give administrators a scientific rationale to back their decisions and interventions, and (3) they facilitate early and more nuanced interventions (than banning or quarantining) to mitigate problematic discourse.

Are Proactive Interventions for Reddit Communities Feasible?

QAnon is a far-right conspiracy theory whose followers largely organize online. In this work, we seek to identify, understand, and learn from the websites that spread QAnon content online. Using website crawling seeded from two of the largest QAnon hotbeds on the Internet, Voat and 8kun, and the resulting hyperlink graph, we curate the largest list of QAnon centered websites to date. From this list of 324 QAnon focused sites, we document the different types of QAnon-focused domains, their domain hosting, and their popularity. We show QAnon websites' connection to mainstream news and misinformation websites, highlighting the outsized role misinformation websites play in spreading the conspiracy. We finally leverage our hyperlink graph and build a random forest classifier to distinguish between misinformation and authentic news sites, achieving 0.98 AUC on a test set. Our results demonstrate new and effective ways to study conspiracy and misinformation on the Internet.

No Calm in The Storm: Investigating QAnon Website Relationships

Malicious Twitter bots are detrimental to public discourse on social media. Past studies have looked at spammers, fake followers, and astroturfing bots, but retweet bots, which artificially inflate content, are not well understood. We present the first study focusing exclusively on retweet bots. We characterize retweet bots that have been uncovered by purchasing retweets from the black market. We detect whether they are fake or genuine accounts involved in inauthentic activities and what they do in order to appear legitimate. We also analyze their differences from human-controlled accounts. From our findings on the nature and life-cycle of retweet bots, we also point out several inconsistencies between the retweet bots used in this work and bots studied in prior works. Our findings challenge some of the fundamental assumptions related to bots and in particular how to detect them.

Characterizing Retweet Bots: The Case of Black Market Accounts

The effects of online social media on critical issues, such as polarization and misinformation, are under scrutiny due to the disruptive consequences that these phenomena can have on our societies. Among the algorithms routinely used by social media platforms, people-recommender systems are of special interest, as they directly contribute to the evolution of the social network structure, affecting the information and the opinions users are exposed to.

In this paper, we propose a novel framework to assess the effect of people recommenders on the evolution of opinions. Our proposal is based on Monte Carlo simulations combining link recommendation and opinion-dynamics models. In order to control initial conditions, we define a random network model to generate graphs with opinions, with tunable amounts of modularity and homophily. Finally, we join these elements into a methodology able to study the causal relationship between the recommender system and the echo chamber effect. Our method can also assess if such relationships are statistically significant. We also show how such a framework can be used to measure, by means of simulations, the impact of different intervention strategies.

Our thorough experimentation shows that people recommenders can in fact lead to a significant increase in echo chambers. However, this happens only if there is considerable initial homophily in the network. Also, we find that if the network already contains echo chambers, the effect of the recommendation algorithm is negligible. Such findings are robust to two very different opinion dynamics models, a bounded confidence model and an epistemological model.

The Effect of People Recommenders on Echo Chambers and Polarization

As  public  discourse  continues  to  move  and  grow  online,conversations  about  divisive  topics  on  social  media  plat-forms have also increased. These divisive topics prompt both contentious  and  non-contentious  conversations.  Although what distinguishes these conversations, often framed as what makes  these  conversations  contentious,  is  known  in  broad strokes, much less is known about the linguistic signature of these conversations. Prior work has shown that contentious content and structure can be a predictor for this task, however,most of them have been focused on conversation in general,very specific events, or complex structural analysis. Addition-ally, many models used in prior work have lacked interpret-ability, a key factor in online moderation. Our work fills these gaps by focusing on conversations from highly divisive topics (abortion, climate change, and gun control), operationalizing a set of novel linguistic and conversational characteristics and user factors, and incorporating them to build interpretable models. We demonstrate that such characteristics can largely improve the performance of prediction on this task, and also enable  nuanced  interpretability.  Our  case  studies  on  these three contentious topics suggest that certain generic linguistic characteristics are highly correlated with contentiousness in conversations while others demonstrate significant contextual influences on specific divisive topics.

Linguistic Characterization of Contentious Topics Online: Case Studies on Controversy in Abortion, Climate Change, and Gun Control

Influencers are key to the nature and networks of information propagation on social media. Influencers are particularly important in political discourse through their engagement with issues, and may derive their legitimacy either solely or in large part through online operation, or have an offline sphere of expertise such as entertainers, journalists etc. To quantify influencers' political engagement and polarity, we use Google's Universal Sentence Encoder (USE) to encode the tweets of 6k influencers and 26k Indian politicians during political crises in India. We then obtain aggregate vector representations of the influencers based on their tweet embeddings, which alongside retweet graphs help compute their stance and polarity with respect to these political issues. We find that influencers engage with the topics in a partisan manner, with polarized influencers being rewarded with increased retweeting and following. Moreover, we observe that specific groups of influencers are consistently polarized across all events. We conclude by discussing how our study provides insights into the political schisms of present-day India, but also offers a means to study the role of influencers in exacerbating political polarization in other contexts.

Divided We Rule: Influencer Polarization on Twitter During Political Crises in India

The ideological asymmetries in contested online spaces have been recently observed, where conservative voices seem to be relatively more pronounced even though liberals have the population advantage on digital platforms. Most of the prior works, however, focused on either a single platform or a single political topic. Whether one ideological group garners more attention across platforms and how the attention dynamics evolve have not been explored. In this work, we present a quantitative study that links collective attention across two social media platforms -- YouTube and Twitter, centered on online activities surrounding videos of controversial political topics including Abortion, Gun control, and Black Lives Matter over 16 months. We propose a set of video-centric metrics to characterize how online attention is accumulated from different political groups over time. Contrasting with prior observations, we find that neither ideological side is on a winning streak: left-leaning videos are overall more viewed, more engaging, but less tweeted than right-leaning videos. The attention unfolds quicker on left-leaning videos, but spans a longer period of time for right-leaning videos. Our network analysis on the early adopters and tweet cascades show that the diffusion for left-leaning videos tends to involve centralized actors, while the tweet cascades for right-leaning videos start earlier in the attention lifecycle. Our findings go beyond the static picture of ideological asymmetries in digital space and provide a set of methods to quantify attention dynamics across different social platforms.

Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case Study

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Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case Study

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Next from ICWSM 2022

Exploring the magnitude and effects of media influence on Reddit moderation

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Covid-19 Vaccine Misinformation Campaigns and Social Media Narratives

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